diff --git a/shopping_api.py b/shopping_api.py
new file mode 100644
index 0000000..a5c151e
--- /dev/null
+++ b/shopping_api.py
@@ -0,0 +1,67 @@
+import requests
+import json
+from bs4 import BeautifulSoup as b
+import pandas as pd
+
+with open('cat_list.txt') as jf:
+    cat_list = json.load(jf)
+finding_service = ['findItemsAdvanced', 'findCompletedItems', 'findItemsByKeywords', 'findItemsIneBayStores', 'findItemsByCategory', 'findItemsByProduct']
+
+class Finding_api:
+    pageNumber = list(range(1, 63))
+
+    # departments = ["3034","93427"]
+
+    def get_ids(self):
+        itemid_results_list = []
+        for categoryID in cat_list[0:2]:
+            params = {
+                "OPERATION-NAME":finding_service[4],
+                "SECURITY-APPNAME":"scottbea-xlister-PRD-6796e0ff6-14862949",
+                "SERVICE-VERSION":"1.13.0",
+                "RESPONSE-DATA-FORMAT":"JSON",
+                "categoryId":categoryID ,
+                "paginationInput.entriesPerPage":"100",
+                "paginationInput.PageNumber":pageNumber[0]
+                }
+            # extract item id here for piping into shopping_test.py
+
+            response = requests.get("https://svcs.ebay.com/services/search/FindingService/v1", params=params)
+            data = response.json()
+            pretty_data = json.dumps(data, indent=2)
+            return data
+
+class Shopping_api:
+
+    def get_item(self):
+
+        params = {
+            "callname":"GetMultipleItems",
+            "appid":"scottbea-xlister-PRD-6796e0ff6-14862949",
+            "version":"671",
+            "responseencoding":"JSON",
+            "ItemID":item_id_results, # you pass in a list? If not then maybe a comma-separated
+            "IncludeSelector":"ItemSpecifics",
+            }
+
+        response = requests.get("https://open.api.ebay.com/shopping?", params=params)
+        data = response.json()
+        pretty_data = json.dumps(data, indent=2)
+
+        names = []
+        values = []
+        nvl = data['Item'][0]['ItemSpecifics']['NameValueList']
+
+        for nvl_dict in nvl:
+            names.append(nvl_dict['Name'])
+            values.append(nvl_dict['Value'])
+
+        nvl_dict = dict(zip(names, values))
+        data.update(nvl_dict)
+        df = pd.json_normalize(data)
+        df.to_csv('big_data.csv')
+
+# Limited to 5000 calls to shopping api per day, and getMultpileitems service maxes out at 20 items
+# per call leaving you 100,000 items per day for you pandas dataframe initially. So you'll have
+# to divide these up into the categories. This will leave you with about 6.25K results per cat. 
+# More than enough data for your dataset. Consider